Ranking functions that rank documents according to their relevance to a given search query are known. In many known systems, query classifiers are used to boost the search results of the ranking function. Known query classifiers utilize machine learning techniques such as Maximum Entropy, Naïve Bayes, Conditional Random Fields, and Support Vector Machines, to model user performance. Typically, the process used for building and deploying models is to gather a lot of data, perform off-line data processing over the entire range of data, build the models, and then deploy the models. These known processes can be computationally expensive. Further, in these processes, there is typically a delay from the time data is gathered to when the data is actually utilized in the deployed models.
Efforts continue in the art to develop ranking functions and ranking function components that provide better search results for a given search query compared to search results generated by search engines using known ranking functions and ranking function components.